Integrated system for designing a user interface

Abstract

The present disclosure is directed to systems and methods for determining which UI features from the gallery of UI features to incorporate in a design environment. For example, the method may include generating a gallery of user interface (UI) features based on a machine learning model trained to analyze usage of different UI features from among a plurality of UI features to identify usage patterns of the different UI features. The method may include receiving user feedback analyzing the gallery of UI features. The method may include determining, based on a combination of the user feedback and the machine learning model, which UI features from the gallery of UI features to incorporate in a design environment. The method may include providing the determined UI features in the design environment accessed over a communications network via a single sign-on process.

What is claimed is: 
     
       1. A computer-implemented method, comprising:
 generating a gallery of user interface (UI) features based on a machine learning model trained to analyze usage of different UI features from among a plurality of UI features to identify usage patterns of the different UI features; 
 receiving user feedback analyzing the gallery of UI features, wherein the user feedback includes respective indications from users of the gallery of UI features whether to include the different UI features in a design environment; 
 determining, based on a combination of the user feedback and the machine learning model, which UI features from the gallery of UI features to incorporate in the design environment, wherein the determining comprises ranking the UI features from the gallery of UI features based on a combination of the respective indications from the users of the gallery of UI features and the usage patterns of the UI features from the gallery of UI features and applying one or more criteria to the ranked UI features, wherein the determined UI features satisfy the one or more criteria; and 
 providing the determined UI features in the design environment accessed over a communications network via a single sign-on process. 
 
     
     
       2. The method of  claim 1 , wherein the machine learning model is further trained to cluster the usage patterns of the different UI features based on a product type. 
     
     
       3. The method of  claim 1 , wherein the machine learning model is further trained to cluster the usage patterns of the different UI features based on a user role. 
     
     
       4. The method of  claim 1 , wherein the respective indications from the users of the gallery of UI features comprises respective votes from the users of the gallery of UI features. 
     
     
       5. The method of  claim 1 , further comprising deleting a UI feature from the gallery of UI features based on the user feedback. 
     
     
       6. The method of  claim 1 , further comprising adding an omitted UI feature to the gallery of UI features based on the user feedback. 
     
     
       7. The method of  claim 1 , wherein, to identify the usage patterns of the different UI features, the machine learning model is further trained to analyze UI features implemented in UIs executing in respective live implementations. 
     
     
       8. The method of  claim 7 , wherein analyzing the UI features implemented in the UIs executing in the respective live implementations comprises analyzing underlying code of the UIs executing in respective live implementations or analyzing a layout of the UIs executing in respective live implementations. 
     
     
       9. The method of  claim 1 , wherein, to identify the usage patterns of the different UI features, the machine learning model is further trained to perform a trend analysis on the usage patterns. 
     
     
       10. A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, cause the at least one computing device to perform operations comprising:
 generating a gallery of user interface (UI) features based on a machine learning model trained to analyze usage of different UI features from among a plurality of UI features to identify usage patterns of the different UI features; 
 receiving user feedback analyzing the gallery of UI features, wherein the user feedback includes respective indications from users of the gallery of UI features whether to include the different UI features in a design environment; 
 determining, based on a combination of the user feedback and the machine learning model, which UI features from the gallery of UI features to incorporate in the design environment, wherein the determining comprises ranking the UI features from the gallery of UI features based on a combination of the respective indications from the users of the gallery of UI features and the usage patterns of the UI features from the gallery of UI features and applying one or more criteria to the ranked UI features, wherein the determined UI features satisfy the one or more criteria; and 
 providing the determined UI features in the design environment accessed over a communications network via a single sign-on process. 
 
     
     
       11. The non-transitory computer-readable device of  claim 10 , wherein the machine learning model is further trained to cluster the usage patterns of the different UI features based on a product type. 
     
     
       12. The non-transitory computer-readable device of  claim 10 , wherein the machine learning model is further trained to cluster the usage patterns of the different UI features based on a user role. 
     
     
       13. The non-transitory computer-readable device of  claim 10 , wherein the respective indications from the users of the gallery of UI features comprises respective votes from the users of the gallery of UI features. 
     
     
       14. The non-transitory computer-readable device of  claim 10 , wherein, to identify the usage patterns of the different UI features, the machine learning model is further trained to analyze UI features implemented in UIs executing in respective live implementations. 
     
     
       15. The non-transitory computer-readable device of  claim 14 , wherein analyzing the UI features implemented in the UIs executing in the respective live implementations comprises analyzing underlying code of the UIs executing in respective live implementations or analyzing a layout of the UIs executing in respective live implementations. 
     
     
       16. A system comprising:
 a memory; and 
 a processor coupled to the memory and configured to:
 generate a gallery of user interface (UI) features based on a machine learning model trained to analyze usage of different UI features from among a plurality of UI features to identify usage patterns of the different UI features; 
 receive user feedback analyzing the gallery of UI features, wherein the user feedback includes respective indications from users of the gallery of UI features whether to include the different UI features in a design environment; 
 determine, based on a combination of the user feedback and the machine learning model, which UI features from the gallery of UI features to incorporate in the design environment, wherein the determining comprises ranking the UI features from the gallery of UI features based on a combination of the respective indications from the users of the gallery of UI features and the usage patterns of the UI features from the gallery of UI features and applying one or more criteria to the ranked UI features, wherein the determined UI features satisfy the one or more criteria; and 
 provide the determined UI features in the design environment accessed over a communications network via a single sign-on process. 
 
 
     
     
       17. The system of  claim 16 , wherein the respective indications from the users of the gallery of UI features comprises respective votes from the users of the gallery of UI features.